An Optimized Density-Based Lane Keeping System for A Cost-Efficient Autonomous Vehicle Platform: AurigaBot V1

Abstract

The development of self-driving cars has garnered significant attention from researchers, universities, and industries worldwide. Autonomous vehicles integrate numerous subsystems, including lane tracking, object detection, and vehicle control, which require thorough testing and validation. Scaled-down vehicles offer a cost-effective and accessible platform for experimentation, providing researchers with opportunities to optimize algorithms under constraints of limited computational power. This paper presents a four-wheeled autonomous vehicle platform designed to facilitate research and prototyping in autonomous driving. Key contributions include (1) a novel density-based clustering approach utilizing histogram statistics for landmark tracking, (2) a lateral controller, and (3) the integration of these innovations into a cohesive platform. Additionally, the paper explores object detection through systematic dataset augmentation and introduces an autonomous parking procedure. The results demonstrate the platform's effectiveness in achieving reliable lane tracking under varying lighting conditions, smooth trajectory following, and consistent object detection performance. Though developed for small-scale vehicles, these modular solutions are adaptable for full-scale autonomous systems, offering a versatile and cost-efficient framework for advancing research and industry applications.